N8python

def random_so(batch, n, *, generator, device):
    matrix = torch.randn((batch, n, n), generator=generator, device=device)
    q, r = torch.linalg.qr(matrix)

    # Sign-correct QR to obtain Haar-random orthogonal matrices.
    diagonal = torch.diagonal(r, dim1=-2, dim2=-1)
    signs = torch.where(diagonal >= 0, 1.0, -1.0)
    q = q * signs.unsqueeze(-2)

    # Force determinant +1, giving SO(N) rather than O(N).

N8python

// N8AO 2.0.0 generated neural Poisson-blur fragment shader
// Variant: Neural-High, 16 denoise taps, perspective camera, standard depth
// Network: 552 inline int8 coefficients, 517 nonzero terms after zero pruning
// This is the exact ShaderMaterial fragment source; Three.js prepends its GLSL3 program prefix.

#define NEURAL_DENOISE
layout(location = 0) out highp vec4 neuralFragColor;
#define gl_FragColor neuralFragColor

		uniform sampler2D sceneDiffuse;

N8python

import argparse
import copy
import glob
import shutil
import time
import types
from pathlib import Path

import mlx.core as mx
import mlx.nn as nn

N8python

from mlx.utils import tree_flatten, tree_map
from mlx_lm import load, generate
import mlx.core as mx
from mlx_lm.utils import (
    dequantize_model,
    fetch_from_hub,
    get_model_path,
    quantize_model,
    save_config,
    save_weights,

N8python

import mlx.core as mx
@mx.compile
def _compute_T1(A):
    """I + A"""
    return mx.eye(A.shape[-1]) + A
@mx.compile
def _compute_T2(A):
    """I + A + A^2/2"""
    A2 = A @ A
    return mx.eye(A.shape[-1]) + A + A2/2

N8python

import torch
import torch.nn as nn
import torch.optim as optim

# Define the differentiable orthonormal linear layer
class OrthonormalLayer(nn.Module):
    def __init__(self, n):
        """
        Initializes a learnable layer with an orthonormal weight matrix.
        :param n: Dimension of the square weight matrix.

N8python

import json
import random
import mlx.optimizers as optim
import mlx.core as mx
import mlx.nn as nn
import numpy as np

from tqdm import tqdm
import time
from datetime import datetime

N8python

def generate_batched(
    model: nn.Module,
    tokenizer: Union[PreTrainedTokenizer, TokenizerWrapper],
    prompt: str,
    batch_size: int,
    *,
    verbose: bool = False,
    formatter: Optional[Callable] = None,
    max_tokens: int = 256,

N8python

from mlx_lm import load
import mlx.core as mx
from mlx.utils import tree_flatten, tree_map, tree_unflatten
import numpy as np
# Copyright © 2023-2024 Apple Inc.

import contextlib
import copy
import glob
import importlib

N8python

from mlx_lm import load
import mlx.core as mx
from mlx.utils import tree_flatten, tree_map, tree_unflatten
import numpy as np
# Copyright © 2023-2024 Apple Inc.

import contextlib
import copy
import glob
import importlib